Gartner Identifies the Top Strategic Trends in Software Engineering for 2025 and Beyond
Gartner,
Inc. announced the top strategic trends in software engineering for 2025 and
beyond. These trends enable organizations to accelerate innovation through
AI-driven automation and adopt efficient, future-ready engineering practices.
“AI-enabled
tools and technologies are fundamentally changing how software is built and
delivered,” said Joachim Herschmann, VP Analyst at Gartner.
“Gartner’s top trends offer leaders a roadmap to harness AI-driven automation,
optimize talent strategies and adopt sustainable, AI-native engineering
practices. Software engineering leaders who act on these trends now will
position their organizations for long-term success.”
As
organizations develop their strategic roadmap for 2025 and beyond, they must
prioritize investments that align with these six strategic trends in software
engineering:
AI-Native Software
Engineering
AI-native
software engineering is transforming the software development life cycle (SDLC)
by embedding AI into every phase, from design to deployment. These practices
enable AI to autonomously or semi-autonomously handle a significant share of
tasks across SDLC.
Gartner
predicts that by 2028, 90% of enterprise software engineers will use AI code assistants, up from less than 14%
in early 2024. The role of developers will shift from
implementation to orchestration, focusing on problem solving and system design,
and ensuring AI tools deliver high-quality outcomes. To succeed, teams must
balance automation with human oversight, considering business criticality, risk
and workflow complexity.
Building LLM-Based
Applications and Agents
Large
language models (LLMs) are transforming software engineering by enabling
applications to interact intelligently and autonomously, similar to human
interactions. Gartner predicts that by 2027, at least 55% of software
engineering teams will be actively building LLM-based features.
“Successfully
building LLM-based applications and agents requires software engineering
leaders to rethink their strategies,” said Herschmann. “This means investing in
upskilling, experimenting with generative AI (GenAI) outputs and
implementing strong guardrails to manage risks.”
GenAI Platform
Engineering
GenAI
platform engineering leverages established platform engineering approaches to
help developers easily discover, integrate and securely use GenAI capabilities within their
applications. Gartner predicts that by 2027, 70% of organizations with platform
teams will include GenAI capabilities in their internal developer platforms.
By
making AI capabilities easily discoverable through self-service developer
portals, prioritizing features based on developer needs and embedding robust
governance and security practices, leaders can ensure their teams build
innovative applications that comply with
organizational standards.
Maximizing Talent
Density
Talent density—the concentration of highly
skilled professionals within teams—has become a key differentiator for
high-performing engineering organizations. When supported by the right culture
and technology strategies, talent-dense teams can operate with greater
efficiency and adaptability, delivering increased customer value.
“To
remain competitive, organizations must move beyond traditional hiring practices
and focus on building teams with high talent density,” said Herschmann.
“Leaders should cultivate a culture of continuous learning and collaboration to
attract and retain skilled professionals who can adapt and grow with evolving
business needs.”
Growth of Open GenAI
Models and Ecosystem
Open
GenAI models are reshaping the enterprise AI landscape by offering greater
flexibility, lower costs and freedom from vendor lock-in.
Unlike
proprietary models, open GenAI models enable organizations to customize,
fine-tune and deploy AI solutions tailored to their specific needs, whether
on-premises or on-cloud. This shift is making advanced AI capabilities more
accessible and cost-effective for a wider range of teams and use cases.
Gartner
predicts that by 2028, 30% of total global enterprise spend on GenAI
technologies will be on open GenAI models tuned for domain-specific use cases.
Green Software
Engineering
Green
software engineering is the discipline of building sustainable software that is
carbon-efficient and carbon-aware. This approach shifts the mindset from
treating the environmental impact of software as an afterthought to
incorporating green practices from the start, in every stage from planning to
production.
“As
organizations build and buy more software as part of digital initiatives, the
increase in carbon footprint from compute-intensive workloads can be at odds
with their sustainability targets,” said Herschmann. “Building intelligent
applications with GenAI is energy intensive, making green software engineering
practices essential.”
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